如何在网络模型训练过程中绘制train_loss和val_accuracy随epoch变化的曲线

        话不多说，直接上代码。

train_losses=[]
val_accuracies = []
train_losses.append(running_loss / train_steps)
val_accuracies.append(val_accurate)
plt.plot(np.arange(1, epochs+1), train_losses, label="train_loss")
plt.plot(np.arange(1, epochs + 1), val_accuracies, label="val_accuracy")
plt.xlabel("Epochs")
plt.title("Train_losses and val_accuracies")
plt.legend()
plt.savefig('./result')
plt.show()

        具体怎么应用我们找个代码示例说着更明白。

    train_losses=[] #创建两个存储train_loss和val_accuracy的列表
    val_accuracies = []
    # epochs = 10
    # best_acc = 0.0
    # save_path = './{}Net.pth'.format(model_name)
    # train_steps = len(train_loader)
    # for epoch in range(epochs):
    #     # train
    #     net.train()
    #     running_loss = 0.0
    #     train_bar = tqdm(train_loader, file=sys.stdout)
    #     for step, data in enumerate(train_bar):
    #         images, labels = data
    #         optimizer.zero_grad()
    #         outputs = net(images.to(device))
    #         loss = loss_function(outputs, labels.to(device))
    #         loss.backward()
    #         optimizer.step()
    # 
    #         # print statistics
    #         running_loss += loss.item()
    # 
    #         train_bar.desc = "train epoch[{}/{}] loss:{:.3f}".format(epoch + 1,
    #                                                                  epochs,
    #                                                                  loss)            
        # validate
        # net.eval()
        # acc = 0.0  # accumulate accurate number / epoch
        # with torch.no_grad():
        #     val_bar = tqdm(val_loader, file=sys.stdout)
        #     for val_data in val_bar:
        #         val_images, val_labels = val_data
        #         outputs = net(val_images.to(device))
        #         predict_y = torch.max(outputs, dim=1)[1]
        #         acc += torch.eq(predict_y, val_labels.to(device)).sum().item()
        # 
        # val_accurate = acc / val_num
        # print('[epoch %d] train_loss: %.3f  val_accuracy: %.3f' %
        #       (epoch + 1, running_loss / train_steps, val_accurate))
        val_accuracies.append(val_accurate) #添加每次的val_accuracy到列表
        train_losses.append(running_loss / train_steps)#添加每次训练的loss

        # if val_accurate > best_acc:
        #     best_acc = val_accurate
        #     torch.save(net.state_dict(), save_path)

    # 训练结束后绘制损失和准确率随着 epoch 变化的图像
    plt.plot(np.arange(1, epochs+1), train_losses, label="train_loss")#绘制train_loss图像
    plt.plot(np.arange(1, epochs + 1), val_accuracies, label="val_accuracy")#绘制val_accuracy图像
    plt.xlabel("Epochs")#x轴对应的标签
    plt.title("Train_losses and val_accuracies")#图像的标题
    plt.legend()#图例
    plt.savefig('./result')#图像保存路径及标题
    plt.show()#展示，一定要先保存再plt.show,否则保存的是空白的图像。